1. Technical Field
This invention relates to image processing and, more particularly, to techniques for detecting moving objects in cluttered scenes.
2. Discussion
A segmentation method is disclosed here which is employed primarily for the acquisition of moving objects, and is particularly applicable to those situations where the signature of the object is wholly or partially obscured by background clutter. This segmentation method also has application to the tracking and aimpoint selection functions of an acquired object.
Basically, automatic or autonomous acquisition is a detection problem. As is well known to those skilled in the art, the two most important considerations in any detection process are false detections (FDs) and missed detections (MDs). It is the goal of any competent designer of detection circuits or systems to minimize the probabilities of FDs and MDs, since the occurrence of either can cause a malfunction in the system which employs the process and thereby seriously reduce its cost-effectiveness.
In general, FDs and MDs trade off against each other; a decrease in the false alarm rate can usually be achieved at the cost of an increase in the frequency of missed detections, and vice versa. Given an irreducible lower bound in performance level achievable by a particular detection method, it is the function of the system designer to perform the trade off so as to achieve maximum effectiveness of the system within imposed constraints.
Quite often the application of predetection or post-detection processing can enhance the detection process. For instance, one can adjust parameters to allow a greater frequency of FDs in order to reduce the probability of MDs, and then resort to post-detection methods (computer algorithms, for instance) to reduce the FD rate.